Etransportation最新文献

The current battery management system is limited to testing external characteristics, leaving the battery's internal status as a “black box”. Advanced characterization techniques and battery sensing technologies are needed to assess the battery's internal state. However, due to their short lifespan, low sensitivity, invasive nature, and high cost, these technologies face challenges in practical applications and commercialization. Here, we propose a smart battery implanted with a potential sensor for in-situ measurement of anode potential, enabling the recognition of severe side reactions and abnormal Li plating behavior. Specifically, the potential sensing material is directly integrated into the battery separator, which provides a reliable potential reference and serves as a sensing terminal. The porous structure of the separator facilitates lithium-ion transport while simultaneously enabling high-accuracy monitoring with non-destructive implantation. Additionally, the potential sensing separator can detect pre-existing or latent defects in the battery at an early stage, which are difficult to discern from the battery's external characteristics in a timely manner. Furthermore, we have developed a multi-point potential sensor monitoring system that can not only monitor the distribution of anode potential but also pinpoint the location of battery defects.

In recent years, the development of electric vehicles (EVs) thermal management systems has underscored the crucial role in ensuring driving safety and optimizing driving range has become increasingly prominent. However, the inherent dynamic complexity of EV operation coupled with automatic control systems, can sometimes lead to unstable behavior, resulting in performance degradation and safety risks for compressors and batteries. To effectively address this issue, an evaluation was conducted on the dynamic control characteristics of an EV thermal management system utilizing CO₂ as the refrigerant in this study. Through mathematical modeling and experimental analysis, the erratic nature of the dynamic thermal process was first identified. The underlying reasons were elucidated, focusing on system control characteristics and intrinsic mechanisms. It was found that control disorder could induce abnormal actions in thermal management system components like compressors and expansion valve, leading to significant performance decline and issues such as liquid carryover in compressor suction. Furthermore, specific control disorder regions of CO₂ heat pumps for EVs were delineated, providing a framework for assessing the likelihood of system control disorder. Notably, control disorder was more likely to occur under conditions of low indoor air flow rate, high ambient temperature, and low supply air temperature. Given the widespread nature of this issue and the lack of suitable solutions, two control disorder suppression schemes were developed using V2X technology and validated through simulation. Results showed that adoption of V2X communication technology prevented an average of 70.1 % COP degradation, ensuring stability and safety of compressors and batteries under various operating conditions. The research provides useful information for exploring the dynamic characteristics of CO₂ thermal management systems, offering a novel approach to enhance the system stability and efficiency.

A surrogate model that predicts thermal runaway (TR) of lithium-ion batteries (LIBs) fast and accurately is essential, yet complex phenomena of TR present significant challenges to achieving adequate performance in both aspects, particularly as traditional finite element models (FEMs) incur significant time and cost. This study proposes a multiphysics-informed deep operator network (MPI-DeepONet) with encoders to address these issues. This proposed neural network aims to predict TR under various thermal abuse conditions, offering a fast and accurate TR prediction surrogate model. In this study, MPI-DeepONet with encoders is trained with virtual data from a multiphysics FEM to overcome the scarcity of actual TR data. The architecture of DeepONet solves interpolation and extrapolation problems, allowing predictions across multiple thermal abuse conditions once trained. The neural network is further enhanced by the supervision of energy balance and chemical reaction equations, ensuring accurate and robust predictions despite limited data by effectively capturing the complex phenomena of TR. Quantitative analysis, compared against actual experiments and ablation studies, confirms the effectiveness of the proposed neural network. Notably, MPI-DeepONet achieves a mean RMSE of 13.2 °C for temperature predictions in the test set, significantly outperforming the 25.4 °C RMSE of purely data-driven DeepONet. This improvement highlights the importance of integrating multiphysics constraints into the neural network. The generality of the proposed neural network is further evidenced by accurate TR prediction in both LFP and NMC cells. The features deployed on the proposed neural network enable real-time quantification of internal temperature distribution and dimensionless concentration of the key components in LIBs, which are challenging to measure directly, achieving speeds at least 10,000 times faster than FEM. The proposed neural network provides comprehensive information for advanced battery management systems to ensure safety and reliability in LIBs, accelerating the digital twin of electric transportation systems through artificial intelligence transformation.

As the electrification trend of vehicles continues, new energy vehicles such as electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) are being equipped with new functional energy storage devices demanding a trade-off between electrical and mechanical property. Accordingly, composite-battery integrated structures which simultaneously carry mechanical resistance and energy-storage capacity, are being explored to offer great potential for the next generation of EVs or PHEVs. Herein, the dynamic responses and failure mechanisms of the integrated structure under the commonly occurring low-velocity impact events are studied both experimentally and numerically. A macro-scale finite element (FE) model was developed by implementing constitutive models of component materials, including lithium‐ion polymer (LiPo) battery cells, polymer foams, and carbon fiber-reinforced polymers (CFRP). The numerical method demonstrates good feasibility and accurately predicts impact behaviors, with the maximum error of the peak impact load not exceeding 8 %. The integrated structures are proven to reduce mechanical damage while maintaining mechanical and electrochemical performance within a range of impacts. The electrical and mechanical behaviors and their correlations were revealed. Sensitivity of the mechanical behaviors and electrical failure to battery arrangement were discussed as well as the structure design on energy absorption capacity. These results hold significant potential for the safety and lightweight design of energy storage composite structures incorporating lithium-ion batteries.

Structure failure of lithium-ion battery (LIB) pack ceiling leads to the unintended release of combustible and poisonous substances during thermal runaway (TR), resulting in personnel injuries and financial losses. However, limited research has been conducted on the mechanism behind pack ceiling failures. In this study, we developed a coupled multiphase fluid-structure interaction (FSI) model to simulate the evolution of up-cover baffle under the TR impact of a 52 Ah NCM battery. Our findings reveal several important insights:1) the maximum force and temperature on the baffle are 13.01 N and 598.5 °C in experiment; 2) the simulation shows that particles exert higher temperature and greater force on the baffle compared to the gas phase; 3) the overall equivalent stress in the stainless-steel baffle surpasses the tensile strength that incurs crack on the baffles. According to the validated model, we find that the baffle structure failure is caused by the thermal stress from particle-structure heat conduction. Furthermore, this observation is applicable to the structure failure problems associated to the thermal runaway of high-density battery that involves enormous particles. In addition, the insulation layer is found to be more effective than the gap distance in protecting the pack ceiling. These findings offer a valuable insight into the structure design of LIB pack, and provide the guidance toward future battery integration technologies.

Environmental pollution and energy scarcity have acted as catalysts for the energy revolution, particularly driving the rapid progression of vehicle electrification. Lithium-ion batteries play a fundamental role as the pivotal components in electric vehicles. Nevertheless, battery sudden death poses substantial challenges to battery design and management. This work comprehensively investigates the failure mechanism of cell sudden death under different degradation paths and its impact on cell performances. Multi-angle characterization analysis shows that lithium plating is the primary failure mechanism of battery sudden death under different degradation paths. However, the formation mechanisms of lithium plating differ in various degradation paths. In the path-L and path-F, the limited lithium intercalation rate in graphite leads to lithium plating, while localized anode drying and uneven potential distribution are the causes in the path-H and path-R. Furthermore, sudden death significantly reduces the cell electrochemical performances and thermal safety, but the cell performance evolution varies under different degradation paths. Sudden death primarily affects the anode interface polarization process in the path-L and path-F, with a more severe impact on cell thermal safety. However, sudden death mainly affects the charge transfer process, with a relatively milder impact on cell thermal safety. These findings can provide valuable insights for optimizing battery design and management.

Safety of lithium-ion battery (LIB) cells throughout the whole lifecycle has drawn enormous research interest. Understanding how cycling ageing affects the mechanical-electrical-thermal responses of LIB cells under mechanical abuse is meaningful for more considerate safety design. In the present study, impact of room temperature ageing on morphology of lithium-ion pouch cell was experimentally explored at first, which clearly identified the deposition phenomenon on electrodes induced by electrolyte consumption. Spherical indentation along out-of-plane direction was carried out on both pristine and aged cells, in which the mechanical-electrical-thermal responses were all monitored. Test results indicate that the mechanical response of the aged cells is quite distinct from the pristine ones, characterized by a rightward shift of the force-displacement curve. Electrical and thermal responses of the aged cells were comparatively less severe. It is inferred that those deposits generated during the ageing process postpone the failure of cells. The short circuit of aged cells behaves relatively tenderly as short contact is alleviated by deposits on the surface of electrodes. By combining the present results with previous researches, correlation between the ageing mechanism and the mechanical abuse failure was sorted for different cells subjected to different ageing processes. It is recognized that changes in mechanical, electrical, and thermal responses of aged cells are highly dependent on both ageing condition and battery configuration.

Ammonia and hydrogen, as carbon-free clean energy, can be converted and applied in various scenarios. They can also be mixed to achieve synergistic efficiency. To promote the carbon-neutral development of heavy-duty vehicles, this paper studies an ammonia-hydrogen powertrain equipped with both a fuel cell and an engine (FCEAP). This powertrain efficiently allocates energy between multiple power sources and exploits the potential of ammonia-hydrogen synergy fuel. The modeling of FCEAP is based on experimental data obtained from engine bench tests, and the control strategy enables real-time control. Additionally, FCEAP undergoes multi-objective co-optimization using the non-dominated sorting algorithm-III (NSGA-III). By optimizing ammonia consumption, acceleration time, and manufacturing cost, Pareto solutions for the configuration and control strategy parameters are obtained. Furthermore, FCEAP is compared to ammonia-hydrogen powertrains equipped with either a fuel cell (FCAP) or an engine (EAP). The trade-off solutions indicate that FCEAP effectively balances energy consumption and manufacturing cost compared with FCAP and EAP. A comprehensive analysis of the energy flow distribution within various ammonia-hydrogen powertrains is conducted, revealing the operational processes and details of each component. The proposed ammonia-hydrogen powertrain represents an important technological pathway for achieving carbon neutrality in the future heavy-duty long-haul trucks industry.

Electric vehicle (EV) technology addresses the challenge of reducing carbon and greenhouse gas emissions. The power battery, which serves as the energy source for EVs, directly impacts their driving range, maximum speed, and service life. Considering the high energy density requirements for future EVs, lithium metal anodes possess several advantages such as high theoretical capacity, high energy and power density, and low electrochemical reduction potential which enable them to be a promising material for next-generation batteries. However, lithium metal anodes suffer from short cycle life and safety concerns due to the formation of dendritic and moss-like metal deposits that impede battery performance and reliability. This review will feature the recent advancement of functional separators to tackle these challenges. Firstly, this review presents a comprehensive review of the growth mechanism of lithium dendrites and delineates the underlying processes leading to battery failure. This aims to deepen understanding, which serves as a fundamental basis for classifying separators. Then, according to the growth of lithium dendrites and the failure process of lithium metal batteries, namely lithium-ion nucleation, growth of lithium dendrites, penetration of lithium dendrites into the separator, thermal runaway and even failure of the battery, four types of functional separators for different stages are proposed. The functions of these types of separators are to prevent the nucleation of lithium ions and regulate the uniform deposition of lithium ions, detect and eliminate dendrites, increase the mechanical strength of the separator and enhance the thermal stability and flame-retardancy of the separators, respectively. Finally, the recent advances of the above strategies are reviewed and discussed, existing critical problems are identified, and the future perspective of functional separators for the safety of lithium metal batteries is also discussed.

This study addresses the common occurrence of cell-to-cell variations arising from manufacturing tolerances and their implications during battery production. The focus is on assessing the impact of these inherent differences in cells and exploring diverse cell and module connection methods on battery pack performance and their subsequent influence on the driving range of electric vehicles (EVs). The analysis spans three battery pack sizes, encompassing various constant discharge rates and nine distinct drive cycles representative of driving behaviours across different regions of India. Two interconnection topologies, categorised as “string” and “cross”, are examined. The findings reveal that cross-connected packs exhibit reduced energy output compared to string-connected configurations, which is reflected in the driving range outcomes observed during drive cycle simulations. Additionally, the study investigates the effects of standard deviation in cell parameters, concluding that an increased standard deviation (SD) leads to decreased energy output from the packs. Notably, string-connected packs demonstrate superior performance in terms of extractable energy under such conditions.